Statistical analyses were conducted using R (v 4.3.1) (https://www.r-project.org/ (accessed on 16 June 2023)) with custom scripts with the available packages in this project. Principal component analysis (PCA) was used to examine the relationship between the composition and the variability of cell metabolites. PCA is an unsupervised technique that reduces the dimensionality of the original data matrix, retaining the maximum amount of variance. Linear discriminant analysis (LDA) is a supervised technique used for classification purposes. Hierarchical cluster analysis (HCA) was also used to analyze the RGB database of the samples. This clustering technique comprises an unsupervised chemometric procedure that involves the measurement of either the distance or similarity between objects to be clustered. Objects are grouped in clusters in terms of their nearness or similarity. The initial assumption is that the nearness of the objects in the p-space of the variables reflects the similarity of their properties. The statistical significance of the difference between the means of the samples was tested using a two-way analysis of variance (ANOVA) with Duncan’s test (p < 0.05) with the IBM SPSS program, version 26.0 (SPSS Inc., Chicago, IL, USA, 2023).
Spss program version 26
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SPSS program version 26 is a software application used for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of SPSS is to enable users to perform a variety of statistical procedures, including regression analysis, factor analysis, and hypothesis testing.
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Lab products found in correlation
19 protocols using spss program version 26
1
Multivariate Analysis of Cell Metabolites
2
Comparative Analysis of Carcass Measurements
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Descriptive statistics (maximum, minimum, mean, median, mode, kurtosis, skewness)
were obtained to compare VCS2000 measured values with dissected values. The
homogeneity of variance was identified through the F-test. A t-test was
performed for Means and SD. Pearson’s correlation coefficient was
obtained to analyze the correlation between the VCS2000 measured value and the
dissected value. A single regression analysis was performed with carcass weight
as the dependent variable and the weight of each part as the independent
variable. All these statistical processes were performed using the SPSS program
version 26.0 (IBM, Armonk, NY, USA).
were obtained to compare VCS2000 measured values with dissected values. The
homogeneity of variance was identified through the F-test. A t-test was
performed for Means and SD. Pearson’s correlation coefficient was
obtained to analyze the correlation between the VCS2000 measured value and the
dissected value. A single regression analysis was performed with carcass weight
as the dependent variable and the weight of each part as the independent
variable. All these statistical processes were performed using the SPSS program
version 26.0 (IBM, Armonk, NY, USA).
3
Microbial Changes in Stored Foods
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Data of changes in chemical composition, microbial population and bacterial community indices during storage was repeatedly compared with Duncan’s test, using the SPSS program version 26.0 (IBM Corp., Armonk, NY, United States). Differences were considered statistically significant only when the probability level was lower than 0.05 (p < 0.05). In addition, Spearman correlation was analyzed among bacterial community compositions and anaerobic fermentation parameters.
4
Epidemiological Study of Camel C. titillator
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The geographical environmental conditions of each pasture and the factors related to the possible epidemic of the disease and their roles in the epidemic of C. titillator were examined.
The prevalence of infestation in different pastures and its correlation with the independent variables (gender, season, husbandry methods, and different age groups of camels) were initially analyzed using the Chi-square test [52 (link), 53 (link)]. Binary logistic regression analysis was then performed on parameters considered significant in statistical analysis to investigate the associations between C. titillator larvae infestation status and pasture location, gender, season, animal feeding method, and age of the study camels [40 , 54 (link)]. All statistical analyses were performed using SPSS program version 26 (IBM, USA). Differences were considered significant at P < 0.05.
The prevalence of infestation in different pastures and its correlation with the independent variables (gender, season, husbandry methods, and different age groups of camels) were initially analyzed using the Chi-square test [52 (link), 53 (link)]. Binary logistic regression analysis was then performed on parameters considered significant in statistical analysis to investigate the associations between C. titillator larvae infestation status and pasture location, gender, season, animal feeding method, and age of the study camels [40 , 54 (link)]. All statistical analyses were performed using SPSS program version 26 (IBM, USA). Differences were considered significant at P < 0.05.
5
Dermatophyte Infection Risk Factors
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Data were double entered into EPI-Data version 3.1 and transferred to Statistical Package for Social Science (SPSS) program version 26 (IBM Company) for analysis. Descriptive statistics of different variables were determined and expressed in the form of texts, tables, and graphs using summary measures such as percentages, mean and median.
Bivariate logistic regression was carried out to identify the associated factors with dermatophyte infection. Variables with a p-value ≤0.25 in the bivariable analysis were candidates for the multivariable model building. The multi-collinearity test was carried out to observe the correlation between predictor variables using standard error and independent variables analysis, and a variable with a standard error of >2 was rejected. The degree of statistical significance was declared at a p-value < 0.05 with 95% confidence intervals.
Bivariate logistic regression was carried out to identify the associated factors with dermatophyte infection. Variables with a p-value ≤0.25 in the bivariable analysis were candidates for the multivariable model building. The multi-collinearity test was carried out to observe the correlation between predictor variables using standard error and independent variables analysis, and a variable with a standard error of >2 was rejected. The degree of statistical significance was declared at a p-value < 0.05 with 95% confidence intervals.
6
Personality Traits Predicting Burnout
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The data was coded into an Excel Program spreadsheet, and then exported to the IBM SPSS Program, version 26 (IBM, Armonk, NY, USA). Descriptive tests were requested to verify that the imported data was error-free. Next, the percentages of appearance of each category were determined in the case of the qualitative variables, and the statistics of central tendency in the case of the quantitative variables. The Alpha reliability index was also calculated using Pearson correlations both for the set of items on the Zimbardo ZTPI scale and for each of the subscales both on that scale and on the MBI-GS. Lastly, three linear regression analyses were performed, one for each of the variables of being burned out that make up each of the factors of the MBI-GS test (emotional exhaustion, cynicism and professional efficacy). The variables that make up the ZTPI personality test (negative past, present hedonistic, future, positive past, and present fatalistic) were considered as predictors. The tables of each regression analysis will allow us to determine which personality variables are present in those subjects who manifest a burnout phenomenon.
7
Inflammatory Markers and Mental Health in COVID-19
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The SPSS program Version 26.0 was used to analyze data. The demographic data of the respondents were summarized using descriptive statistics expressed in terms of number and percentage (%). Normal distribution of continuous variables was tested by Kolmogorow–Smirnov test. Data conforming to the normal distribution were expressed as x ± s, while the measurement data not conforming to the normal distribution was expressed as median and quartile intervals. Categorical variables were expressed as the frequency (%). Clinical characteristics were categorical variables, and Chi-square tests and Fisher exact test (used to sample size <40) were used to detect group differences. Inflammatory markers and NCT were continuous variables. The Mann–Whitney U tests were used to analyze continuous and abnormal distributed variables of inflammatory markers and NCT between the patients with and without mental disorders and sleep problems. Independent sample T test was used to analyze continuous and normal distributed variables of inflammatory markers between the patients with and without mental disorders and sleep problems. Pearson correlation analysis and Spearman rank correlation analysis were utilized to examine the relationship of mental health status, sleep quality, and laboratory data in confirmed patients. Significant level was set as P < .05 for all tests (2-sided).
8
eHealth Literacy Determinants Analysis
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For statistical analysis, the eHEALS was accepted as the dependent variable. Statistical Package for the Social Sciences (SPSS) Program version 26.0 was used for statistical analysis. Continuous variables were expressed as mean ± standard deviation (SD) and median. Categorical variables were expressed as numbers and percentages (%). Kolmogorov–Smirnov and Shapiro–Wilk tests were performed for normality analysis of the data and Skewness and Kurtosis values of the scales with p < 0.05 were analyzed. It was accepted that the values with Skewness and Kurtosis values between ±1.5 were normally distributed, and the values not between ±1.5 were not normally distributed.
Since the data in the research group did not show normal distribution, the Mann–Whitney U test and Kruskal–Wallis test were used in data analysis. Chi-square and Fisher’s exact tests were used to compare categorical variables between groups. Correlation (Spearman) analysis was used for the relationship between continuous variables. Logistic regression analysis was performed to predict the level of eHealth literacy according to the independent variables, model fits were evaluated, and the variables that contributed significantly to the model were examined. In statistical analyses, p ˂ 0.05 was considered significant.
Since the data in the research group did not show normal distribution, the Mann–Whitney U test and Kruskal–Wallis test were used in data analysis. Chi-square and Fisher’s exact tests were used to compare categorical variables between groups. Correlation (Spearman) analysis was used for the relationship between continuous variables. Logistic regression analysis was performed to predict the level of eHealth literacy according to the independent variables, model fits were evaluated, and the variables that contributed significantly to the model were examined. In statistical analyses, p ˂ 0.05 was considered significant.
9
Chi-square Analysis Using SPSS
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SPSS program, version 26, was used to perform the Chi-square test (significance level: p < 0.05).
10
Statistical Analysis of Quantitative Data
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The SPSS program version 26 was used. The prospective data collection permitted an analysis without the presence of missing values. In the description of the quantitative variables, the values of the mean and standard deviation were given, or the medians and interquartile range when the normality conditions were not met. The categorical variables were described in absolute numbers and percentages. The univariate statistical analysis of the quantitative variables, with independent groups, was carried out using the Student’s T-test, provided its conditions of application were met otherwise, the Mann Whitney U or the Kruskal–Wallis test was applied. For categorical variables, Pearson’s X2 test or Fisher’s exact test was used, depending on the conditions. A p value < 0.05 was considered statistically significant, with a confidence interval of 95%.
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